In the description of nnet in CRAN projects(https://cran.r-project.org/web/packages/nnet/nnet.pdf) it says that nnet fits a single hidden layer:
Description: Fit single-hidden-layer neural network, possibly with skip-layer connections
Is it possible for me to specify the number of hidden layers using nnet? My understanding was that my selection of hidden layers and number of neurons in the hidden layer were the parameters that can be changed to improve a model. Is it true that it could help a model to add/remove hidden layers? Or, are there separate areas of application of single-layered and multi-layered neural networks?
I am new to ANN. I am working on a classification model with training sample size of 55000 x 54. Thanks in advance!
Simple answer NO, nnet
always has a single layer where you specify the number of nodes. You can find more information in a similar question here. You will need to use other packages such as neuralnet
or something more sophisticated like h20
or MXNet
.
Regarding parameters to improve the model, there are many different parts of neural networks beyond the raw architecture (i.e. layers, nodes). These include optimization functions, activation functions, batchsizes, among many others. You likely want to consult some further resources on using neural networks.